Association Between Cardiometabolic Risk Factors and Body Mass Index Based on Diagnosis and Treatment Codes in an Electronic Medical Record Database

BACKGROUND: Managed care organizations (MCOs) have access to treatment and diagnosis information from administrative claims data but generally have limited or no access to clinical information about laboratory values or biometric values such as body mass index (BMI) or waist circumference. Thus, MCOs are generally unable to identify overweight patients with cardiometabolic risk factors that put them at a high risk of poor outcomes. The National Heart, Lung, and Blood Institute defines normal body weight as a BMI (ratio of weight in kilograms to height in meters squared [kg/m2]) from 18.5 to 24.9 kg/m2, overweight as 25.0 to 29.9 kg/m2, and obesity as a BMI of 30 kg/m2 or greater. Current guidelines for weight-loss pharmacotherapy, including U.S. Food and Drug Administration-approved label indications, specify use in patients with a BMI of 30 kg/m2 or greater, or a BMI greater than 27 kg/m2 and at least 1 concomitant cardiometabolic risk factor such as controlled hypertension, diabetes, or dyslipidemia. OBJECTIVES: To evaluate the association of cardiometabolic risk factors with BMI as recorded in a database of electronic medical records (EMRs). METHODS: Each patient had a minimum look-back observation period of 2 years from the last date of activity in the EMR. Patients with a BMI of 18 kg/m2 or greater recorded in the EMR at any time during the 10-year period from January 1996 through December 2005 were stratified into groups by the number of cardiometabolic risk factors and by individual cardiometabolic risk for those with just 1 risk factor. Cardiometabolic risk factors were identified from diagnoses and prescription orders in the EMR associated with high triglyceride levels, low high-density lipoprotein cholesterol (HDL-C) levels, type 2 diabetes, or hypertension. Unadjusted and adjusted odds ratios (ORs) of having a BMI greater than27 kg/m2 were calculated for each risk factor group and for patients with no risk factors. Using logistic regression analysis, ORs were adjusted for age, gender, insurance type, region, medications associated with weight gain or weight loss, and diseases that modify weight. RESULTS: A total of 499,593 patients with a BMI of 18 kg/m2 or greater were identified; 56.4% (n = 281,988) had a BMI greater than 27 kg/m2, whereas 43.6% (n = 217,605) had a BMI between 18 and 27 kg/m2. Compared with patients with no risk factors (n = 289,960), patients with 1-4 risk factors (n = 209,633) were significantly more likely to have a BMI greater than 27 kg/m2; 48.4% of patients without cardiometabolic risk factors had a BMI greater than 27 kg/m2, compared with 63.3%, 79.8%, 84.6%, and 88.5% for patients with 1-4 cardiometabolic risk factors, respectively (all comparisons P less than 0.001). Adjusted ORs for having a BMI greater than 27 kg/m2 were 2.64 (95% confidence interval [CI] = 2.51-2.77) for type 2 diabetes, 2.21 (95% CI = 2.05-2.37) for elevated triglycerides, 1.91 (95% CI = 1.88-1.94) for hypertension, and 1.45 (95% CI = 1.29-1.63) for low HDL-C. Adjusted ORs for having a BMI greater than 27 kg/m2 were 3.58 (95% CI = 3.47-3.69), 4.24 (95% CI = 3.93-4.59), and 5.07 (95% CI = 3.77-6.81) for patients with any 2, 3, and 4 risk factors respectively, relative to patients with no cardiometabolic risk factors. CONCLUSIONS: For patients with cardiometabolic risk factors, compared with patients with no risk factors, the odds of having a BMI greater than 27 kg/m2 were multiplied by 1.45-5.07, depending on the type and number of risk factors. Diagnoses and treatment indicators for cardiometabolic risk factors are potential indicators of obesity.


R E S E A R C H
• Managed care organizations (MCOs) generally have access to treatment and diagnosis information from administrative claims data but may have limited or no access to clinical information about laboratory values or biometric readings. In the absence of clinical data about body mass index (BMI) and waist circumference, MCOs may be unable to identify overweight patients with cardiometabolic risk factors that put them at an increased risk of poor outcomes. • Patients with a BMI > 27 kg/m 2 (ratio of weight in kilograms to height in meters squared) and at least 1 cardiometabolic risk factor are potential candidates for weight-loss pharmacotherapy according to U.S. Food and Drug Administration-approved labeling. • Stafford and Radley found that 71% of patients who received weight-loss pharmacotherapy had no reported medical conditions other than obesity. • Although the association between BMI and cardiometabolic risk factors has been extensively studied, there is no published research on prediction of BMI from administrative claims data.

What is already known about this subject
What this study adds • The presence of cardiometabolic risk factors multiplies the odds of having a BMI > 27 kg/m 2 by 1.45-5.07, depending on the type and number of risk factors. The results demonstrate the association between cardiometabolic risk factors and the likelihood of being overweight using an electronic medical record that contains biometric and clinical laboratory data. • Further development of this concept may allow health plans to identify patients who may be potential candidates for weightloss pharmacotherapy based on whether they have been diagnosed or treated for cardiometabolic risk factors identifiable in administrative claims. C ardiovascular disease is a leading cause of mortality in the United States and a significant driver of health care costs. 1 Health care providers and payers recognize the value of aggressively identifying those at risk and treating the underlying risk factors for cardiovascular disease to help prevent poor outcomes. The identification of underlying cardiometabolic risk factors (lipids, blood pressure, and glucose indicators) is potentially important to slowing cardiovascular disease progression and minimizing the economic consequences for a health plan.
The third report of the National Cholesterol Education Program-Adult Treatment Panel (NCEP-ATP III) identified the cardiometabolic risk factors as central obesity, dyslipidemia (hypertriglyceridemia and low levels of high-density lipoprotein cholesterol [HDL-C]), impaired glucose tolerance, and elevated blood pressure. As a risk factor, obesity is independently associated with cardiometabolic risk, and obesity contributes to the development of the other risk factors. [2][3][4] Thus, reduction in weight can both directly minimize obesity related risk and indirectly reduce other cardiometabolic risks by improving low-density lipoprotein cholesterol and triglyceride profiles, reducing blood pressure, and improving insulin sensitivity.
In the United States, some payers provide coverage for weightloss interventions, including bariatric surgery, individual dietary counseling, and to a lesser extent, weight-loss drugs; however, coverage varies. 5 Although evidence has been published on the short-term clinical and economic benefits of weight loss in reducing risk for other cardiometabolic risks, 6,7 without readily available evidence from their own health care systems, payers may be skeptical about coverage of weight-loss interventions. 8 Additionally, appropriate targeting of these interventions may be difficult to achieve; Stafford and Radley found that 71% of U.S. patients who received weight-loss pharmacotherapy in 1996 had no reported medical conditions other than obesity. 9 Even when weight-loss interventions are covered, reimbursement for such interventions may require prior approval based on documentation of obesity, to avoid inappropriate use in individuals not at a body mass index (BMI) associated with medical risk. However, payer efforts to identify overweight and obese patients with cardiometabolic risk for weight-loss interventions are generally limited to analyses of administrative (reimbursement) claims data.
Claims data include diagnosis and treatment (procedure) codes, which are valuable in helping payers identify patients with commonly treated conditions such as hypertension, diabetes, and dyslipidemias, but are of limited value in identifying patients with obesity. Only 38% of obese patients receive a formal medical diagnosis for that condition. 10 Claims do not capture biometric data such as weight and waist circumference or BMI, which would be the most effective means of identifying obese patients. Thus, in the absence of clinical data about BMI or weight and waist circumference, managed care organizations may be unable to identify overweight patients with cardiometabolic risk factors that put them at a high risk of poor outcomes.
The purpose of this study is to identify individuals diagnosed with cardiometabolic risk factors, including diabetes, hypertension, or dyslipidemia, based on indicators readily available in claims data (dispensed drugs and diagnoses) and evaluate whether these individuals are also overweight based on biometric data available in an electronic medical record (EMR) database, but not generally available to payers. This work examines the association in EMRs between risk factors, as indicated by diagnoses and prescription orders, and BMI to suggest the possible feasibility of permitting health plans to use information commonly available in administrative claims data to estimate the likelihood of obesity in their members.

■■ Methods Study Design
This study was an observational cross-sectional study of patients, about 64% of whom were treated in primary care physician (PCP) practice settings.

Source
The data source used for this project was the General Electric (GE) Centricity research database. Centricity (GE Healthcare, Waukesha, Wisconsin) is an EMR system that enables ambulatory care physicians and clinical staff to document patient encounters, streamline clinical workflow, and securely exchange clinical data with other providers, patients, and information systems. Centricity EMR is used by more than 20,000 clinicians to manage about 30 million patient records in 49 states, making it a widely used ambulatory care EMR. The data for this study were provided by a subset of approximately 9,000 Centricity practitioners from more than 100 practice sites located in 35 states that contributed medical record data for the patients they treat to the Medical Quality Improvement Consortium to create a research database. Approximately 64% of the submitting clinicians are PCPs, and the others are in various medical specialties. This EMR system replaces the paper medical record for the patients in the participating medical offices. The resulting research database provides information reflective of the clinical data captured in the practice setting, including diagnoses, chief complaints, medication orders, medication lists (patient-reported prescription and over-the-counter drug use), laboratory orders and results, and biometric readings. Data are collected centrally and go through a quality control process to clean the data and remove invalid values.

Study Population and Time Frame
The study population was drawn from the GE EMR population that had any activity from January 1996 through December 2005. Subjects were included in the analysis if they met the following criteria: 1. Aged 20 years or older on their last activity date to ensure that patients were at least 18 years of age during the entire 2-year observation period 2. Evidence of a clinical encounter within the previous 2 years, defined as a documentation of interaction between the physician office and patient for any reason. The observation period for each subject was the 2-year period before the patient's last activity date. 3. At least 1 BMI measure recorded or calculated during the 2-year observation period that was at least 18 kg/m 2 (ratio of weight in kilograms to height in meters squared).

Study Variables
BMI values in the EMR database were recorded by clinicians or were calculated automatically from patient height and weight values. If there was more than 1 BMI value in the 2-year period for each patient, the BMI value closest to the midpoint (365 days) was used. NCEP-ATP III 11 and International Diabetes Federation 12 guidelines use waist circumference as a measure of central or abdominal obesity, but waist circumference is rarely available in clinical data. Therefore, BMI was used as a proxy measure and calculated from the patients' height and weight measurements. Several studies have concluded that BMI and waist circumference are highly correlated to each other, and that each independently contributes a significant risk for metabolic syndrome and chronic diseases. [13][14][15][16] The National Heart, Lung, and Blood Institute defines overweight as a BMI of 25-29.9 kg/m 2 and obesity as a BMI > 30 kg/m 2 . Weight-loss drugs are approved by the U.S. Food and Drug Admi nis tration as adjuncts to diet and exercise in patients with a BMI of 30 kg/m 2 or greater, or a BMI > 27 kg/m 2 for patients with 1 or more concomitant risk factors, including hypertension, dyslipidemia, and type 2 diabetes. 17 An objective of this study is to provide data on the risk of patients being overweight when they have other cardiometabolic risk factors. Thus, in calculating the odds ratios (ORs) for the outcome measure, a BMI > 27 kg/m 2 was used.
Independent variables used in this study were the presence of a diagnosis or prescription order indicative of any of 4 risk factors: high triglycerides, low level of high-density lipoprotein cholesterol (HDL-C), hypertension, and type 2 diabetes (Appendix 1). Patient records were evaluated for the 2-year observation period to ascertain whether patients had 1 or more International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes documented as a problem anywhere in the EMR during the 2-year observation period or 1 or more prescription orders for any of the risk factors. Based only on the diagnosis and prescription data for the 2-year observation period, patients were categorized into groups: (a) no risk factors versus at least 1 risk factor, (b) 4 groups for each of the individual risk factors, and (c) 3 groups of multiple-risk factors (2, 3, or 4 risk factors) without regard to the specific risk-factor combinations.

Association Between Cardiometabolic Risk Factors and Body Mass Index Based on Diagnosis and Treatment Codes in an Electronic Medical Record Database
Patients with individual risk factors were identified to assess the association of the individual risk factors with a BMI > 27 kg/m 2 . In addition, patients were categorized by age group: 18-30 years, 31-45 years, 46-64 years, 65-79 years, and 80 years or older, representing important age categories related to cardiometabolic risk and insurance coverage.
Additional demographic, clinical, and treatment covariates were captured, including gender, geographic location, and insurance status. Medications in the EMR database are identified by name and by Medi-Span Generic Product Identifier (GPI); covariates of cardiometabolic risk factors were identified by GPI drug category for medications that are (a) associated with weight gain or weight loss, (b) affect lipid profiles, or (c) associated with the development of cardiometabolic risk factors. These medications included antidepressants, oral antidiabetic agents and insulin, anticonvulsants, antipsychotics, antiretrovirals, progesterone, corticosteroids, and weight-loss agents. Diseases that were controlled for included hypothyroidism (identified using ICD-9-CM codes 243 for congenital hypothyroidism and 244.x for acquired hypothyroidism), type 1 diabetes (identified using ICD-9-CM codes 250.x1 and 250.x3) and polycystic ovaries (identified using ICD-9-CM code 256.4).

Analysis
Descriptive statistics for patients in each of the risk factor groups by BMI category were computed to show characteristics such as age, gender, geographic region of residence, and insurance status of each group. Between-group differences were analyzed using a Pearson chi-square test of significance.
Unadjusted and adjusted ORs for having a BMI > 27 kg/m 2 were calculated for patient groups with individual cardiometabolic risk factors (reference categories: patients without the risk factors) and for groups defined by the count of risk factors (1, 2, 3, or 4, with 0 risk factors as the reference category). To calculate the adjusted ORs, logistic regression was used, adjusting for age group, gender, geographic region of residence (Southeast, Midwest, or West, with Northeast as the reference group), insurance type (Medicare, Medicaid, self-pay, or other/unknown, with commercial insurance as the reference group), and drug and disease covariates. Predictive probabilities of having a BMI > 27 kg/m 2 were obtained from the adjusted logistic regression model and compared against the actual observations in the dataset for accuracy of predictions. The receiver operating characteristic (ROC) curve procedure in SPSS 16.0 (SPSS Inc., Chicago, IL) was used to calculate a c-statistic for the adjusted logistic regression model. The area under the ROC curve (c-statistic) is a measure of the discriminatory power of the logistic regression. The value of c-statistic ranges from 0 to 1, with 1 indicating the highest (perfect) predictive ability and 0.5 indicating a model with a predictive ability not better than random chance. 18 This study was deemed exempt by the University of Utah Institutional Review Board in a letter received September 1, 2006, based on a determination that the study was a retrospective analysis in a Health Insurance Portability and Accountability Act-compliant, deidentified database.

■■ Results
The eligible population for this study included 3,216,323 patients aged 20 years or older on their last EMR activity date. Of the eligible population, 1,519,639 (47.3%) met the criteria of at least 1 interaction with a physician office within the previous 2 years. Of these patients, 505,693 (33.3%) had at least 1 BMI value; 499,593 patients (32.9%) who had at least 1 BMI measurement of 18 kg/m 2 or greater were included in the study. Figure 1 shows the selection of the final study population. Table 1 describes the population demographics. Mean (SD) age of the population was 52.8 (17.5) years, and women comprised 61.9% of the population. The proportion of patients with a BMI > 27 kg/m 2 increased with age up to 65 years (43.3%, 56.5%, and 64.4% for age groups 18-30 years, 31-45 years, and 46-64 years, respectively) and then began to fall (58.1% and 39.0% for patients aged 65-79 years and 80 years or older, respectively). A higher proportion of male patients (62.4%) had a BMI > 27 kg/m 2 than did female patients (52.8%). Medicaid patients (62.1%) were more likely to have a BMI > 27 kg/m 2 than were patients enrolled in other insurance types (commercial, 56.6%; Medicare, 54.8%; self-pay: 51.4%; and other/unknown, 57.6%). The proportion of patients with a BMI > 27 kg/m 2 was higher in the Southeast (59.1%) and Midwest (58.3%) than in the Northeast (53.9%) and West (52.4%). P values for all comparisons were statistically significant at P < 0.001 due to the large sample size.
Considering all cardiometabolic risk factors except the dependent variable of weight, 58.0% (289,960) of the study sample had no cardiometabolic risk factors and 42.0% (209,633) had 1 or more cardiometabolic risk factors. Overall, and for each individual risk factor, the proportion of patients identified by drug treatment alone (using the criteria shown in Appendix 1) was notably higher (66.9%) than the proportions identified by diagnosis (11.4%) or by both diagnosis and drug treatment (21.7%, Table 2). A similar trend was observed in patients with multiple cardiometabolic risk factors. The proportion of patients identified by treatment was significantly higher than the proportions identified by diagnosis or both diagnosis and treatment for each risk factor.
Among patients with 1 cardiometabolic risk factor, the proportion of patients with a BMI > 27 kg/m 2 was higher in patients with diabetes (75.2%) than in patients with elevated triglyceride levels (70.6%), hypertension (62.2%), or decreased HDL-C levels (61.9%, P < 0.001; Table 1). In patients with multiple risk factors (2, 3, and 4 risk factors) the percentage of patients with a BMI > 27 kg/m 2 ranged from 79.8% to 88.5%.
When evaluating BMI by the presence of medication and disease covariates, the most notable finding was related to the concurrent presence of medications that influence body weight. The use of oral antidiabetic agents that cause weight gain or weight loss, and other weight-loss drugs was approximately 3 times higher for the group with a BMI > 27 kg/m 2 (weight gain, 7.5%; weight loss, 7.7%) than for the group with a BMI of 27 kg/m 2 or lower (weight gain, 2.5%; weight loss, 2.0%). Table 3 presents unadjusted ORs and adjusted ORs derived from logistic regression models, assessing the association between the 4 cardiometabolic risk factors and a BMI > 27 kg/m 2 . The model was adjusted for age, gender, geographic region of residence, insurance type, and concomitant medications likely to influence weight. Indicators for type 1 diabetes and polycystic ovaries were dropped from the regression model due to multicollinearity (i.e., high correlation with variables indicating use of antidiabetic agents).
In the insurance type category, Medicaid patients were significantly more likely (OR = 1.32, 95% CI = 1.27-1.37), and self-pay patients were significantly less likely (OR = 0.92, 95% CI = 0.87-0.98) to have a BMI > 27 kg/m 2 than were patients with commercial insurance. The adjusted results show that the odds of having a BMI > 27 kg/m 2 was significantly higher for patients having any single cardiometabolic risk factor, including elevated triglyceride levels (OR = 2.21, 95% CI = 2.05-2.37),

Association Between Cardiometabolic Risk Factors and Body Mass Index Based on Diagnosis and Treatment Codes in an Electronic Medical Record Database
factors in either BMI group. The adjusted regression model had a statistically significant (P < 0.001) c-statistic value of 0.667 (95% CI = 0.665-0.668), which showed a moderate discriminatory ability of the model to identify patients with a BMI > 27 kg/m 2 .

■■ Discussion
Health care payers face a lack of information, which is available to the health care provider, when attempting to identify patients with select conditions and risk factors based only on administrative claims data. In the absence of clinical data that would provide information about BMI or weight and waist circumference, it is difficult for payers to identify overweight patients who are at a high risk of poor cardiovascular outcomes and thus potential candidates for weight loss intervention.
Thus, the purpose of this work was to define a study population by means of an EMR database only by the data available in administrative claims to determine associated BMI. Our results demonstrated that certain patient characteristics could, with moderate success, identify patients at risk of having a BMI > 27 kg/m 2 based on whether an individual has been diagnosed or treated for cardiometabolic risk factors identifiable by administrative claims. The adjusted logistic regression model in this study provides a statistically significant moderate discriminatory potential (c-statistic: 0.67) to predict patients with a BMI > 27 kg/m 2 by cardiometabolic risk factors. In every category, patients with 1 or more cardiometabolic risk factors identified only by diagnosis or treatment indicators that are typically available in claims data were more likely (OR = 1.45-5.07) to have a BMI > 27 kg/m 2 than were patients without those cardiometabolic risk factors. Among individual risk factors, type 2 diabetes or elevated triglyceride levels had the greatest association with a BMI > 27 kg/m 2 . The odds of having a BMI > 27 kg/m 2 were multiplied by 2.64 for patients with type 2 diabetes and 2.21 for patients with elevated triglyceride levels, compared with patients without risk factors. Furthermore, patients with hypertension or low HDL-C had 1.91 and 1.45 times the odds of a BMI > 27 kg/m 2 , respectively, compared with patients without risk factors. This risk of having a BMI > 27 kg/m 2 becomes more pronounced for patients with 2 or more risk factors, for whom the adjusted OR increases from 3.58 with 2 risk factors to 5.07 with 4 risk factors.
The associations between being overweight to obese and the development of hypertension and dyslipidemias identified in this study are similar to those found in prospective cohort studies, which helps validate these findings. For example, an analysis based on the Framingham Heart Study, which followed patients for up to 44 years, found that the risk of developing hypertension was more than 2.5 times as high (relative risk [RR] = 2.63, 95% CI = 2.20-3.15) for obese women (BMI of 30 kg/m 2 or greater) and more than 2 times as high (RR = 2.23, 95% CI = 1.75-2.84) for obese men than for women and men of normal weight. 19 The risk of developing type 2 diabetes was 36% higher in obese women (RR = 1.36, 95% CI = 1.03-1.78) and 85% higher in obese men (RR = 1.85, 95% CI = 1.31-2.26) than for women and men of normal weight. Similarly, the San Antonio Heart Study also found an association between BMI and the development of type 2 diabetes. 20 For each standard deviation increase in weight, the odds of developing type 2 diabetes increased 51% in women (OR = 1.51, 95% CI = 1.21-1.90) and 69% in men (OR = 1.69, 95% CI = 1.07-2.65).
Recent research (2007) has also explored the impact of body weight, alone versus as a component of metabolic syndrome, in relation to risk of incident cardiovascular disease events over a 10-year follow-up period from the Women's Health Study. 21 These results indicated that, among women without metabolic syndrome, there was no significant increase in RR for those who were overweight (BMI of 25-29.9 kg/m 2 , RR = 1.08, 95% CI = 0.87-1.33) and a somewhat increased risk for those who were obese (BMI of 30 kg/m 2 or greater, RR = 1.58, 95% CI = 1.21-2.08). However, the risk of incident cardiovascular disease was much greater for women with metabolic syndrome (RRs of 2.40 for normal-weight women, 3.01 for women with a BMI of 25-29.9 kg/m 2 and 2.89 for women with a BMI of 30 kg/m 2 or greater). These findings support our work in recognizing that patients who are overweight and have indicators of hypertension, dyslipidemia, or diabetes are at higher risk of cardiovascular events, and therefore may have greater benefit from weight intervention, than patients who are overweight alone. This targeted benefit can have important implications for the value of policies that restrict  From the perspective of population management, these findings may help health systems allocate resources and facilitate interventions to promote weight loss in patients with high cardiometabolic risk. We believe that this is the first study to evaluate the ability to use cardiometabolic risk factors as a predictor of whether patients are obese or overweight using only the data that would be available in a pharmacy and medical claims database. A few studies have used administrative or survey data to evaluate associations between patient demographics or cardiometabolic risk and obesity, but none of these studies evaluated whether cardiometabolic risk factors predict obesity. [22][23][24] For example, Godley et al. evaluated the prevalence of cardiometabolic risk factors in a population of patients with both hypertension and type 2 diabetes, using claims data supplemented with medical record data for a subset of the study population; rates of obesity, dyslipidemia, and tobacco use were documented. 24 Hollenbeak et al. and Finkelstein et al. used National Health and Nutrition Examination Survey (NHANES) data to study obesity and cardiometabolic risk. Hollenbeak et al. evaluated the predictive utility of basic demographic data in predicting components of the metabolic syndrome, including abdominal obesity, in the absence of clinical data, finding significant associations among abdominal obesity, age, and gender. 22 Finkelstein et al. found that obesity increased the risk for the development of diabetes and hyperlipidemia. 23 In the present study, 50.2% (141,583) of patients whose BMI was greater than 27 kg/m 2 had either an ICD-9-CM diagnosis or were on treatment for 1 of the nonobesity cardiometabolic risk factors. Although a large proportion of patients with a BMI > 27 kg/m 2 had only 1 risk factor (n = 99,715; 35.4%), a substantial minority (14.8%) had 2 or more risk factors. Presence of 2 or more risk factors multiplied the odds of having a BMI > 27 kg/m 2 by factors of 3-5. In those patients with 1 risk factor and a BMI > 27 kg/m 2 , the prevalence of hypertension (31.3%) was highest however the adjusted OR (2.64) for having a BMI > 27 kg/m 2 was greatest in patients with type 2 diabetes. As an example, patients in a health plan with a cardiometabolic risk factor such as type 2 diabetes or dyslipidemia and a BMI > 27 kg/m 2 may be a target for weight-loss interventions, including weight-controlling medications. A recent (June 2008) guidance from the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom recommends coverage for rimonabant as an adjunct to diet and exercise in patients who have had inadequate response to, or are intolerant of, orlistat and sibutramine and who have either a BMI of 30 kg/m 2 or greater and no risk factors or a BMI > 27 kg/m 2 and at least 1 risk factor such as type 2 diabetes or dyslipidemia. 25 The NICE guidance notes that "steatorrhoea as a consequence of not adhering to dietary advice should not be considered as intolerance to orlistat," and treatment with rimonabant should be continued beyond 6 months only if the patient has lost at least 5% of his or her initial body weight before starting rimonabant.

Odds Ratios of Having a BMI > 27 kg/m 2 Among Patients Aged 18 Years or Older by Demographic Characteristics, Cardiometabolic Risk Factor Diagnosis, and Prescription Orders
Recognizing the role of obesity in cardiometabolic risk, National Institutes of Health guidelines recommend treatment with a weight-loss agent for patients with a BMI of 30 kg/m 2 or greater without additional risk factors or with a BMI of 27 kg/m 2 or greater in patients with at least 1 concomitant risk factor. who have been unable to lose weight or maintain weight loss with conventional nondrug therapies. 17 The treatment cutoff of a BMI > 27 kg/m 2 for patients with cardiometabolic risk factors was used in this study, because in the application of these findings, all patients considered for weight-loss pharmacotherapy will have at least 1 additional cardiometabolic risk factor. 17 The results of the present study indicate that approximately 80% of patients with 2 or more risk factors as identified by ICD-9-CM codes or national drug code numbers in an administrative claims database would be expected to have a BMI > 27 kg/m 2 . These findings, if validated by additional research, may help to support the rationale for initiating weight-loss interventions, including pharmacotherapy in patients with 2 or more cardiometabolic risk factors identified from administrative claims data.
An additional important consideration is the economic impact of potential savings from BMI reduction in patients in these various risk categories. A limitation of the EMR database is that cost data are not included. However, if the characteristics of the patients in each risk group can be matched to patients in administrative claims, the costs of each risk group can be determined and the economic impact of weight reduction to decrease risk can be measured. We are currently pursuing this work and plan to present the results in a future manuscript. Ultimately, the results from this work may have the potential to help health plans with access to only medical and pharmacy claims data to determine the potential economic benefit of weight-loss intervention in their patients with cardiometabolic risk.

Limitations
Foremost among the limitations of this study is the absence of a test of validity of the proposed method of identifying overweight and obese persons from administrative claims data only. For example, it is well known that drugs prescribed and captured in an EMR, which were the most common indication of cardiometabolic risk factors in our database, may not be purchased or used by patients. In addition, the predictive validity of our model has not been established in any population other than the sample in which the predictive weights (ORs) were calculated, and the c-statistic of 0.667 for the present sample indicated only moderate success in predicting BMI from risk factors found in EMR data. For this model to be usable by health plans, its predictive accuracy should be improved and validated in other health plan populations, perhaps by eliminating from the model risk factors that have only slight association with a BMI > 27 kg/m 2 (e.g., low HDL-C level with no other risk factors).

Association Between Cardiometabolic Risk Factors and Body Mass Index Based on Diagnosis and Treatment Codes in an Electronic Medical Record Database
Second, we did not examine the relationship of actual use of weight-loss medications and presumptive appropriate use as determined by BMI. The 2,721 overweight patients with EMR documentation of use of at least 1 weight-loss medication represented 1.0% of the patients with a BMI > 27 kg/m 2 and 1.9% of the patients with a BMI > 27 kg/m 2 and at least 1 cardiometabolic risk factor. Our data would have been more informative if we had stratified the use of weight-loss medications by BMI > 27 kg/m 2 and BMI > 30 kg/m 2 .
Third, patients were selected from a population with a documented BMI measurement. Requiring a BMI value in the EMR data may have introduced bias because physicians may be more likely to measure height so that a BMI can be calculated and input by the physician or automatically calculated by the EMR in patients who are overweight or who have other cardiometabolic risk factors, compared with normal weight patients without obvious cardiovascular risk factors. Thus, our sampling method may have skewed the population to higher risk and thereby overestimated the ability to predict BMI from these associated cardiometabolic risk factors.
Fourth, although we used data elements commonly available in claims data, we did not actually obtain them from administrative claims; we obtained them from an EMR. It is unknown how well the EMR data used in the present study would translate into administrative claims stored by health plans. For example, diagnoses codes in an EMR, which document care provided to a patient, might not match the codes billed by the provider and stored in administrative claims data.

■■ Conclusions
This study assessed the association between BMI and common cardiometabolic risk factors in the interest of developing a methodology to use administrative claims data to identify patients who may benefit from weight-loss interventions, as demonstrated by large prospective cohort studies. Further validation of the predictive value of this work may help target weight-loss interventions.